Statistical analysis with missing data
Statistical analysis with missing data
C4.5: programs for machine learning
C4.5: programs for machine learning
Enhancing data quality in data warehouse environments
Communications of the ACM
Component-based data mining frameworks
Communications of the ACM
Mining Optimized Gain Rules for Numeric Attributes
IEEE Transactions on Knowledge and Data Engineering
Data mining from 1994 to 2004: an application-orientated review
International Journal of Business Intelligence and Data Mining
Discovering patterns of missing data in survey databases: An application of rough sets
Expert Systems with Applications: An International Journal
Multiple instance learning for classifying students in learning management systems
Expert Systems with Applications: An International Journal
Learning from socio-economic characteristics of IP geo-locations for cybercrime prediction
International Journal of Business Intelligence and Data Mining
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Survey data are often incomplete. Incomplete data are often mistreated and damages of missing values in survey are often overlooked in data mining. This study proposes a classification-based data mining approach to assessing the extent of damage of missing values in survey. Using this approach, an incomplete observation is translated into fuzzy observations. These fuzzy observations are used to test the classifier that has been trained by the complete data set of the survey. The test results provide a base for discovering knowledge about the implication of missing data and the quality of the survey.